SoroushVahidi/scidocs huggingface dataset:
Evolving neural networks to play checkers without relying on expert knowledge
(1999)

An experiment was conducted where neural networks compete for survival in an evolving population based on their ability to play checkers. More specifically, multilayer feedforward neural networks were used to evaluate alternative board positions and games were played using a minimax search strategy. At each generation, the extant neural networks were paired in competitions and selection was used to eliminate those that performed poorly relative to other networks. Offspring neural networks were created from the survivors using random variation of all weights and bias terms. After a series of 250 generations, the best-evolved neural network was played against human opponents in a series of 90 games on an internet website. The neural network was able to defeat two expert-level players and played to a draw against a master. The final rating of the neural network placed it in the "Class A" category using a standard rating system. Of particular importance in the design of the experiment was the fact that no features beyond the piece differential were given to the neural networks as a priori knowledge. The process of evolution was able to extract all of the additional information required to play at this level of competency. It accomplished this based almost solely on the feedback offered in the final aggregated outcome of each game played (i.e., win, lose, or draw). This procedure stands in marked contrast to the typical artifice of explicitly injecting expert knowledge into a game-playing program.
Verifying Anaconda's expert rating by competing against Chinook: experiments in co-evolving a neural checkers player
(2002)

Since the early days of arti5cial intelligence, there has been interest in having a computer teach itself how to play a game of skill, like checkers, at a level that is competitive with human experts. To be truly noteworthy, such e7orts should minimize the amount of human intervention in the learning process. Recently, co-evolution has been used to evolve a neural network (called Anaconda) that, when coupled with a minimax search, can evaluate checker-boards and play to the level of a human expert, as indicated by its rating of 2045 on an international web site for playing checkers. The neural network uses only the location, type, and number of pieces on the board as input. No other features that would require human expertise are included. Experiments were conducted to verify the neural network’s expert rating by competing it in 10 games against a “novice-level” version of Chinook, a world-champion checkers program. The neural network had 2 wins, 4 losses, and 4 draws in the 10-game match. Based on an estimated rating of Chinook at the novice level, the results corroborate Anaconda’s expert rating. c © 2002 Elsevier Science B.V. All rights reserved.
Decentralized Decision Making in the Game of Tic-tac-toe
(2006)

Traditionally, the game of Tic-tac-toe is a pencil and paper game played by two people who take turn to place their pieces on a 3times3 grid with the objective of being the first player to fill a horizontal, vertical, or diagonal row with their pieces. What if instead of having one person playing against another, one person plays against a team of nine players, each of whom is responsible for one cell in the 3times3 grid? In this new way of playing the game, the team has to coordinate its players, who are acting independently based on their limited information. In this paper, we present a solution that can be extended to the case where two such teams play against each other, and also to other board games. Essentially, the solution uses a decentralized decision making, which at first seems to complicate the solution. However, surprisingly, we show that in this mode, an equivalent level of decision making ability comes from simple components that reduce system complexity
A self-learning evolutionary chess program
(2004)

A central challenge of artificial intelligence is to create machines that can learn from their own experience and perform at the level of human experts. Using an evolutionary algorithm, a computer program has learned to play chess by playing games against itself. The program learned to evaluate chessboard configurations by using the positions of pieces, material and positional values, and neural networks to assess specific sections of the chessboard. During evolution, the program improved its play by almost 400 rating points. Testing under simulated tournament conditions against Pocket Fritz 2.0 indicated that the evolved program performs above the master level.
Machine learning - an artificial intelligence approach
(1982)

Research in the area of learning structural descriptions from examples is reviewed, giving primary attention to methods of learning characteristic descrip­ tions of single concepts. In particular, we examine methods for finding the maximally-specific conjunctive generalizations (MSC-generalizations) that cover all of the training examples of a given concept. Various important aspects of structural learning in general are examined, and several criteria for evaluating structural learning methods are presented. Briefly, these criteria include (i) ade­ quacy of the representation language, (ii) generalization rules employed, computational efficiency, and (iv) flexibility and extensibility. Selected learning methods developed by Buchanan, et al., Hayes-Roth, Vere, Winston, and the authors are analyzed according to these criteria. Finally, some goals are sug­ gested for future research.
Implementing the Chinese Wall Security Model in Workflow Management Systems
(2010)

The Chinese wall security model (CWSM) was designed to provide access controls that mitigate conflict of interest in commercial organizations, and is especially important for large-scale interenterprise workflow applications. This paper describes how to implement the CWSM in a WfMS. We first demonstrate situations in which the role-based access control model is not sufficient for this, and we then propose a security policy language to solve this problem, also providing support for the intrinsic dynamic access control mechanism defined in the CWSM (i.e., the dynamic binding of subjects and elements in the company data set). This language can also specify several requirements of the dynamic security policy that arise when applying the CWSM in WfMSs. Finally we discuss how to implement a run-time system to implement CWSM policies specified by this language in a WfMS.
Filter Bank Common Spatial Pattern (FBCSP) in Brain-Computer Interface
(2008)

In motor imagery-based brain computer interfaces (BCI), discriminative patterns can be extracted from the electroencephalogram (EEG) using the common spatial pattern (CSP) algorithm. However, the performance of this spatial filter depends on the operational frequency band of the EEG. Thus, setting a broad frequency range, or manually selecting a subject-specific frequency range, are commonly used with the CSP algorithm. To address this problem, this paper proposes a novel filter bank common spatial pattern (FBCSP) to perform autonomous selection of key temporal-spatial discriminative EEG characteristics. After the EEG measurements have been bandpass-filtered into multiple frequency bands, CSP features are extracted from each of these bands. A feature selection algorithm is then used to automatically select discriminative pairs of frequency bands and corresponding CSP features. A classification algorithm is subsequently used to classify the CSP features. A study is conducted to assess the performance of a selection of feature selection and classification algorithms for use with the FBCSP. Extensive experimental results are presented on a publicly available dataset as well as data collected from healthy subjects and unilaterally paralyzed stroke patients. The results show that FBCSP, using a particular combination feature selection and classification algorithm, yields relatively higher cross-validation accuracies compared to prevailing approaches.
Coping with child sexual abuse among college students and post-traumatic stress disorder: the role of continuity of abuse and relationship with the perpetrator.
(2010)

OBJECTIVE
The purpose of this study was to examine the effects of child sexual abuse (CSA) on the use of coping strategies and post-traumatic stress disorder (PTSD) scores in young adults, as well as the role of avoidance and approach coping strategies in those PTSD scores in CSA victims. The role of coping strategies was studied by considering their possible interactive effect with the continuity of abuse and the relationship with the perpetrator; the effect of coping strategies on PTSD was also compared between CSA victim and non-CSA victim participants.


METHOD
The sample was comprised of 138 victims of CSA and another 138 participants selected as a comparison group. Data about child sexual abuse were obtained from a questionnaire developed for this purpose. Coping strategies were assessed with the How I Deal with Things Scale (Burt & Katz, 1987), while PTSD scores were assessed with the "Escala de Gravedad de Síntomas del Trastorno de Estrés Postraumático" (Severity of Symptoms of PTSD Scale; Echeburúa et al., 1997).


RESULTS
Participants who had been victims of CSA showed significantly higher PTSD scores and lower approach coping strategies scores. However, differences in avoidance coping strategies between groups were not consistent and did not always follow the expected direction. Only the use of avoidance coping strategies was related to PTSD, participants who used these showing higher scores. The effects of avoidance strategies were stronger in continued than in isolated abuse, in intrafamilial than in extrafamilial abuse and in CSA victims than in non-victims.


CONCLUSIONS
These results confirm the idea of CSA as a high-risk experience that can affect the victim's coping strategies and lead to PTSD to a lesser or greater extent depending on the coping strategy used. Moreover, the role of these strategies varies depending on whether or not the participant is a victim of CSA and on the characteristics of abuse (continuity and relationship with the perpetrator).


PRACTICE IMPLICATIONS
In terms of intervention, a reduction of avoidance-type strategies appears to have a beneficial effect, especially in the case of intrafamilial and/or continued CSA victims. The encouragement of "spontaneous" approach strategies (devised by the victim herself, without counseling) would probably not lead to more positive outcomes in terms of PTSD symptomatology. However, encouraging CSA survivors to engage in therapy aimed at developing effective approach strategies, as other studies have suggested, may help reduce PTSD symptoms.
An approch for monitoring and smart planning of urban solid waste management using smart-M3 platform
(2014)

Solid waste management is one of the most important challenges in urban areas throughout the world and it is becoming a critical issue in developing countries where a rapid increase in population has been observed. Waste collection is a complex process that requires the use of large amount of money and an elaborate management of logistics. In this paper an approch to smart waste collection is proposed able to improve and optimize the handling of solid urban waste. Context of smart waste management requires interconnection among heterogeneous devices and data sharing involving a large amount of people. Smart-M3 platform solves these problems offering a high degree of decoupling and scalability.Waste collection is made by real-time monitoring the level of bin's fullness through sensors placed inside the containers. This method enables to exempt from collecting semi-empty bins. Furthermore, incoming data can be provided to decisional algorithms in order to determine the optimal number of waste vehicles or bins to distribute in the territory. The presented solution gives important advantages for both service providers and consumers. The formers could obtain a sensible cost reduction. On the other hand, users may benefit from a higher level of service quality. In order to make users feel closer to their community, they can interact with the system to be aware about the fulness state of the nearest bins. Finally, a mechanism for collecting “green points” was introduced for encouraging citizens to recycle.
Wearable Glove-Type Driver Stress Detection Using a Motion Sensor
(2017)

Increased driver stress is generally recognized as one of the major factors leading to road accidents and loss of life. Even though physiological signals are reported as the most reliable means to measure driver stresses, they often require the use of unique and expensive sensors, which produce dynamic and varying readings within individuals. This paper presents a novel means to predict a driver’s stress level by evaluating the movement pattern of the steering wheel. This is accomplished by using an inertial motion unit sensor, which is placed on a glove worn by the driver. The motion sensor selected for this paper was chosen because for its low cost and the fact that it is least affected by environmental factors as compared with a physiological signal. Experiments were conducted in three different environmental scenarios. The scenarios were classified as “urban,” “highway,” and “rural,” and they were chosen to simulate contrasting stress conditions experienced by the driver. In this paper, skin conductance and driver self-reports served as a reference stress to predict the driver’s stress level. Galvanic skin response, a well-known stress indicator, was captured along the driver’s palm and the readings were transmitted to a mobile device via low energy Bluetooth for further processing. The results revealed that indirect measurement of steering wheel movement with an inertial motion sensor could obtain accuracies up to an average rate of 94.78%. This demonstrates the opportunity for inclusion of motion sensors in wireless driver assistance systems for ambulatory monitoring of stress levels.
The Role of Mental Health Professionals in Gender Reassignment Surgeries: Unjust Discrimination or Responsible Care?
(2014)

Recent literature has raised an important ethical concern relating to the way in which surgeons approach people with gender dysphoria (GD): it has been suggested that referring transsexual patients to mental assessment can constitute a form of unjust discrimination. The aim of this paper is to examine some of the ethical issues concerning the role of the mental health professional in gender reassignment surgeries (GRS). The role of the mental health professional in GRS is analyzed by presenting the Standards of Care by the World Professional Association of Transgender Health, and discussing the principles of autonomy and non-discrimination. Purposes of psychotherapy are exploring gender identity; addressing the negative impact of GD on mental health; alleviating internalized transphobia; enhancing social and peer support; improving body image; promoting resilience; and assisting the surgeons with the preparation prior to the surgery and the patient’s follow-up. Offering or requesting psychological assistance is in no way a form of negative discrimination or an attack to the patient’s autonomy. Contrarily, it might improve transsexual patients’ care, and thus at the most may represent a form of positive discrimination. To treat people as equal does not mean that they should be treated in the same way, but with the same concern and respect, so that their unique needs and goals can be achieved. Offering or requesting psychological assistance to individuals with GD is a form of responsible care, and not unjust discrimination. This journal requires that authors assign a level of evidence to each article. For a full description of these Evidence-Based Medicine ratings, please refer to the Table of Contents or the online Instructions to Authors www.springer.com/00266 .
Semantics and service technologies for the automatic generation of online MCQ tests
(2018)

Active learning requires that students receive a continuous feedback about their understanding. Multiple-Choice Questions (MCQ) tests have been frequently used to provide students the required feedback and to measure the effectiveness of this learning model. To construct a test is a challenging task, which is time consuming and requires experience. For these reasons, research efforts have been focused on the automatic generation of well-constructed tests. The semantic technologies have played a relevant role in the implementation of these testgeneration systems. Nevertheless, the existing proposals present a set of drawbacks that restrict their applicability to different learning domains and the type of test to be composed. In this paper, we propose a service-oriented and semantic-based system that solves these drawbacks. The system consists of a dynamic strategy of generating candidate distractors (alternatives to the correct answer), a set of heuristics for scoring the distractors' suitability, and a selection of distractors that considers the difficulty level of tests. Besides, the final version of tests is created using the Google Form service, a de-facto standard for elaborating online questionnaires.
Solving traveling salesman problems via artificial intelligent search techniques
(2012)

The traveling salesman problem (TSP) is one of the most intensively studied problems in computational mathematics and combinatorial optimization. It is also considered as the class of the NPcomplete combinatorial optimization problems. By literatures, many algorithms and approaches have been launched to solve such the TSP. However, no current algorithms that can provide the exactly optimal solution of the TSP problem are available. This paper proposes the application of AI search techniques to solve the TSP problems. Three AI search methods, i.e. genetic algorithms (GA), tabu search (TS), and adaptive tabu search (ATS), are conducted. They are tested against ten benchmark real-world TSP problems. As results compared with the exactly optimal solutions, the AI search techniques can provide very satisfactory solutions for all TSP problems. Key-Words: Traveling Salesman Problem, Genetic Algorithm, Tabu Search, Adaptive Tabu Search
Conjunctive representations in learning and memory: principles of cortical and hippocampal function.
(2001)

The authors present a theoretical framework for understanding the roles of the hippocampus and neocortex in learning and memory. This framework incorporates a theme found in many theories of hippocampal function: that the hippocampus is responsible for developing conjunctive representations binding together stimulus elements into a unitary representation that can later be recalled from partial input cues. This idea is contradicted by the fact that hippocampally lesioned rats can learn nonlinear discrimination problems that require conjunctive representations. The authors' framework accommodates this finding by establishing a principled division of labor, where the cortex is responsible for slow learning that integrates over multiple experiences to extract generalities whereas the hippocampus performs rapid learning of the arbitrary contents of individual experiences. This framework suggests that tasks involving rapid, incidental conjunctive learning are better tests of hippocampal function. The authors implement this framework in a computational neural network model and show that it can account for a wide range of data in animal learning.
Estimation of Detection Thresholds for Redirected Walking Techniques
(2010)

In immersive virtual environments (IVEs), users can control their virtual viewpoint by moving their tracked head and walking through the real world. Usually, movements in the real world are mapped one-to-one to virtual camera motions. With redirection techniques, the virtual camera is manipulated by applying gains to user motion so that the virtual world moves differently than the real world. Thus, users can walk through large-scale IVEs while physically remaining in a reasonably small workspace. In psychophysical experiments with a two-alternative forced-choice task, we have quantified how much humans can unknowingly be redirected on physical paths that are different from the visually perceived paths. We tested 12 subjects in three different experiments: (E1) discrimination between virtual and physical rotations, (E2) discrimination between virtual and physical straightforward movements, and (E3) discrimination of path curvature. In experiment E1, subjects performed rotations with different gains, and then had to choose whether the visually perceived rotation was smaller or greater than the physical rotation. In experiment E2, subjects chose whether the physical walk was shorter or longer than the visually perceived scaled travel distance. In experiment E3, subjects estimate the path curvature when walking a curved path in the real world while the visual display shows a straight path in the virtual world. Our results show that users can be turned physically about 49 percent more or 20 percent less than the perceived virtual rotation, distances can be downscaled by 14 percent and upscaled by 26 percent, and users can be redirected on a circular arc with a radius greater than 22 m while they believe that they are walking straight.
Story Cloze Ending Selection Baselines and Data Examination
(2017)

This paper describes two supervised baseline systems for the Story Cloze Test Shared Task (Mostafazadeh et al., 2016a). We first build a classifier using features based on word embeddings and semantic similarity computation. We further implement a neural LSTM system with different encoding strategies that try to model the relation between the story and the provided endings. Our experiments show that a model using representation features based on average word embedding vectors over the given story words and the candidate ending sentences words, joint with similarity features between the story and candidate ending representations performed better than the neural models. Our best model achieves an accuracy of 72.42, ranking 3rd in the official evaluation.